# pandas

## merge two dataframe

Append the columns of df2 to df1

```python
df1.join(df2)
```


## select columns

Selecting columns based on their name

```python
df['hue'] # single column

df[['alcohol','hue']] # multiple columns
```

Selecting a subset of columns based on difference of columns

```python
df[df.columns.difference([‘alcohol’,’hue’])]
```

## rename column

```python
df.rename(columns = {'old_col1':'new_col1', 'old_col2':'new_col2'}, inplace = True)
```

## add column

```python
tutors = ['William', 'Henry', 'Michael', 'John', 'Messi']
df2 = df.assign(TutorsAssigned=tutors)
```

OR

```python
df2=df.assign(Discount_Percent=lambda x: x.Fee * x.Discount / 100)
```

OR

```python
ff['Discounted_Price'] = df.apply(lambda row: row.Cost - (row.Cost * 0.1), axis = 1)
```

## create data frame

```python
data = [10,20,30,40,50,60]
  
# Create the pandas DataFrame with column name is provided explicitly
df = pd.DataFrame(data, columns=['Numbers'])
```

OR

```python
data = [['tom', 10], ['nick', 15], ['juli', 14]]
  
# Create the pandas DataFrame
df = pd.DataFrame(data, columns=['Name', 'Age'])
```

OR

```python
data = {'Name': ['Tom', 'nick', 'krish', 'jack'],
        'Age': [20, 21, 19, 18]}
  
df = pd.DataFrame(data)
```

OR

```python
data = [
    {"name": "Alice", "age": 25, "city": "New York"},
    {"name": "Bob", "age": 30, "city": "Los Angeles"},
    {"name": "Charlie", "age": 35, "city": "Chicago"}
]

# Create a DataFrame
df = pd.DataFrame(data)
```

## filter rows

```python
df[df.apply(lambda x: x['b'] > x['c'], axis=1)]
```

## process or transform a column

```python
df['Col4'] = df.apply(lambda row:", ".join([(val if val[0]=='a' else "["+val+"]") for val in row if not pd.isna(val)]), axis=1)
```

## test dataframe equality

```python
from pandas.testing import assert_frame_equal
df1 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]})
assert_frame_equal(df1, df1)
```